Perioperative patient-specific factors-based nomograms predict short-term periprosthetic bone loss after total hip arthroplasty

Abstract

Background: Although medical intervention of periprosthetic bone loss in the immediate postoperative period was recommended, not all the patients experienced periprosthetic bone loss after total hip arthroplasty (THA). Prediction tools that enrolled all potential risk factors to calculate an individualized prediction of postoperative periprosthetic bone loss were strongly needed for clinical decision-making. Methods: Data of the patients underwent primary unilateral cementless THA between April 2015 and October 2017 in our center were retrospectively collected. Candidate variables included demographic data and bone mineral density (BMD) in spine, hip and periprosthetic regions that measured 1 week after THA. Outcomes of interest included the risk of postoperative periprosthetic bone loss in Gruen zone 1, 7 and total zones in the 1st postoperative year. Nomograms were presented based on multiple logistic regressions via R language. 1000 Bootstraps were used for internal validation. Results: 563 patients met the inclusion criteria were enrolled, and the final analysis was performed in 427 patients (195 male and 232 female) after the exclusion. The mean BMD of Gruen zone 1, 7, and total were decreased by 4.1%, 6.4%, and 1.7% at the 1st year after THA, respectively. 61.1% of the patients (261/427) experienced bone loss in Gruen zone 1 at the 1st postoperative year, while there were 58.1% (248/427) in Gruen zone 7 and 63.0% (269/427) in Gruen zone total. Bias-corrected c-index for risk of postoperative bone loss in Gruen zone 1, 7 and total zones in the 1st postoperative year were 0.700, 0.785, and 0.696, respectively. The most highly influential factors for the postoperative periprosthetic bone loss were primary diagnosis and BMD in the corresponding Gruen zones at the baseline. Conclusions: To the best of our knowledge, our study represented the first time to use the nomograms in estimating the risk of postoperative periprosthetic bone loss with adequate predictive discrimination and calibration. Those predictive models would help surgeons to identify high-risk patients who may benefit from anti-bone-resorptive treatment on the early postoperative period effectively. It is also beneficial for patients, as they can choose the treatment options based on a reasonable expectation following surgery.

Development and internal validation of a conventional ultrasound-based nomogram for predicting malignant nonmasslike breast lesions

“…Candidate variables with a P value <0.1 after multivariable logistic analysis in the screening step were included in the nomogram. The relative importance of each predictor in the model was determined by subtracting the degrees of freedom from the Wald chi-square value (12). Odds ratios and 95% confidence intervals were also calculated for each variable.…”

“…Referring to previous studies (13,14), we obtained predicted probabilities for the original sample based on each estimated model of bootstrap and concordance index (C-index) calculation. The bias-corrected C-index was defined as the average of these bootstrap C-indices, representing the ability to distinguish between patients who experience an event from those who do not (12). The C-index was measured on a scale of 0.5 (no better than chance) to 1 (perfect discrimination) (14).…”